Multi-Spatial Measurement Points Based Structural Health Monitoring in the Tendons of an Offshore Aquaculture Farm’s Fish Cage

Abstract Offshore fish farms are one type of aquaculture facilities developed for providing large quantities of improved-quality aqua-culture products. Important components such as nets/tendons of the cages in offshore farms may get damaged due to severe environmental conditions. Fish escape through the damaged nets with dire economic/biological consequences. Thus, early detection of these damages is important. Currently, monitoring the offshore fish farms’ structural health is costly, time-consuming, sporadic as it is based on divers and remote operating vehicles. The present case study investigates the detection of a damaged vertical tendon in an offshore fish farm’s cage via an efficient and automated method. This method offers a remote, continuous, economical monitoring with a constant stream of integrity data as it is equipped with multiple AutoRegressive models based on simulated displacement data from two spatial measurement points on the fish cage. Healthy and damage cases under changing current speed and wave height are considered, with damages corresponding to degradation (fatigue damage) along the whole tendon and at specific points in the tendon. The degradation is simulated by stiffness reduction. The results show the method detecting all the 184 simulations for the damage cases and 34 out of 36 simulations for the healthy cases.